Patch-based crack detection in black box road images using deep learning

Somin Park, Seongdeok Bang, Hongjo Kim, Hyoungkwan Kim

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

This paper proposes a method for patch-based crack detection of black box road images, for efficient road pavement monitoring. The proposed method is based on deep learning and consists of two modules: road extraction and crack detection. The road extraction module uses the segmentation process of a Fully Convolutional Network (FCN) called FCN-8s to leave only the road area in the image. The crack detection module performs patch-based crack detection on the extracted road area using a convolutional neural network. To the best of the authors’ knowledge, the proposed method is the first attempt to detect road cracks of black box images, which are not orthogonal but skewed actual road images.

Original languageEnglish
Publication statusPublished - 2018 Jan 1
Event35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018 - Berlin, Germany
Duration: 2018 Jul 202018 Jul 25

Other

Other35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018
CountryGermany
CityBerlin
Period18/7/2018/7/25

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Building and Construction

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  • Cite this

    Park, S., Bang, S., Kim, H., & Kim, H. (2018). Patch-based crack detection in black box road images using deep learning. Paper presented at 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018, Berlin, Germany.